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Volume 34 Issue 3
Jun.  2015
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YANG Qi-yong, JIANG Zhong-cheng, YUAN Dao-xian, JIANG Yong-jun, SHEN Li-na. Spatial autocorrelation analysis of soil water content in a karst region of Guangxi Province[J]. CARSOLOGICA SINICA, 2015, 34(3): 260-265. doi: 10.11932/karst20150309
Citation: YANG Qi-yong, JIANG Zhong-cheng, YUAN Dao-xian, JIANG Yong-jun, SHEN Li-na. Spatial autocorrelation analysis of soil water content in a karst region of Guangxi Province[J]. CARSOLOGICA SINICA, 2015, 34(3): 260-265. doi: 10.11932/karst20150309

Spatial autocorrelation analysis of soil water content in a karst region of Guangxi Province

doi: 10.11932/karst20150309
  • Publish Date: 2015-06-25
  • Mashan county, located in the middle Guangxi Zhuang Autonomous Region, southwestern China, was selected as the study area. Based on the plentiful information from field surveys, soil sampling and laboratory analysis, we were studied the spatial autocorrelation coefficients, correlation distances and spatial patterns of soil water content in topsoil (0–20 cm) using semi-variances and Moran’ s Istatistics. The results show that the mean value of soil water content is 16.97%. Soil water content shows a moderate spatial autocorrelation within the distance of 78.8 km, which is affected by the constitutive and random factors. (2) Moran index of soil water content in the study area is 0.43, suggesting that the soil water content possesses spatial autocorrelation. In the ranges of 0-21.7 km and 31-34 km, the values of Moran′s Iof soil water content are greater than 0, implying positive spatial autocorrelation; while in the ranges of 21.7-31 km and 34-45 km, the values are negative, indicating negative spatial autocorrelation. Lisa cluster maps show that there are spatial aggregation areas and spatial isolated areas of the soil water content. The “high-high” spatial aggregation areas cluster in the northeast of Mashan county and “low-low” spatial aggregation clustered in the southeast. There are bigger risk of short of soil water content in the “low-low” spatial aggregation and “high-low” spatial isolated areas.

     

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  • [1]
    Bellamy P H, Loveland P J, Bradley R L, et al. Carbon losses from all soils across England and Wales 1978-2003[J]. Nature, 2005, 437: 245-248.
    [2]
    Cliff A D, Ord J K. Spatial process: Models and Applications[J]. London, UK: Pion, 1973:178.
    [3]
    Waser M N, Mitchell R J. Nectar standing crops in delphinium nelsonii flowers: Spatial autocorrelation among plants[J]. Ecology, 1990, 71 (1): 116-123.
    [4]
    Martin D. An aeeseement of surface and zonal models of population. Geographical Information Systerms, 1996, 10 (8): 973-989.
    [5]
    梁二,王小彬,蔡典雄,等. 河南省土壤有机碳分布空间自相关分析[J]. 应用生态学报,2007, 18(6):1305-1310.
    [6]
    霍霄妮, 李红, 孙丹峰, 等. 北京耕作土壤水分含量的空间自相关分析[J]. 环境科学学报, 2009,29(6): 1339-1344.
    [7]
    杨奇勇,杨劲松,余世鹏,等. 不同尺度下耕地土壤Cr含量的空间自相关性分析[J]. 应用与环境生物学报,2011,17 (3): 393-397.
    [8]
    McGrath D,Zhang C S.Spatial distribution of soil organic carbon concentrations in grassland of Ireland [J].Applied Geochemistry,2003,18(10):1629-1639.
    [9]
    杨泉,赵成章,史丽丽,等.祁连山地甘肃臭草斑块土壤水分的空间自相关分析[J]. 生态学杂志,2014,33(3):716-722.
    [10]
    周亮广, 梁虹. 喀斯特地区水资源承载力评价研究:以贵州省为例[J]. 中国岩溶,2006,25(1):23-28.
    [11]
    王家文,周跃,肖本秀,等. 中国西南喀斯特土壤水分特征研究进展[J] . 中国水土保持,2013,2:37-42.
    [12]
    Boyer D G, Wright R J, Feldhake C M, et al.Soil spatial variability relationships in steeply sloping acid soil environment[J]. Soil Sci,1991,161:278-287.
    [13]
    Chopin P, Blazy J M. Assessment of regional variability in crop yields with spatial autocorrelation: Banana farms and policy implications in Martinique[J]. Agriculture, Ecosystems & Environment, 2013, 181(4):12-21.
    [14]
    Chen F, Du D S. Application of the integration of spatial statistical analysis with GIS to the analysis of regional economyic analysis[J]. Geo-Spatial Information Science, 2004, 7(4):262-267.
    [15]
    Griffith D A, Chun Y. Spatial Autocorrelation in Spatial Interactions Models: Geographic Scale and Resolution Implications for Network Resilience and Vulnerability[J]. Networks and Spatial Economics, 2014: 1-29.
    [16]
    Moran P. The Interpretation on Statistical Maps[J]. Journal of the Royal Statistical Society, Series,1948, 37 (10): 243-251.
    [17]
    陈翠英,江永真.土壤养分空间变异性的随机模拟及其应用[J]. 农业机械学报,2006,37(12):67-70,95.
    [18]
    杨奇勇,蒋忠诚,罗为群,等. 岩溶峰丛洼地山体阴影区域植被指数的随机模拟[J]. 农业机械学报,2013,44(5):232-237.
    [19]
    司涵,张展羽,吕梦醒,等. 小流域土壤氮磷空间变异特征分析[J]. 农业机械学报,2014, 45(3):90-96.
    [20]
    Anselin L. GeoDa 0.9 User's Guide. Spatial Analysis Laboratory, University of Illinois, Urbana-Champaign, IL. 2003.
    [21]
    胡伟,邵明安,王全九. 黄土高原退耕坡地土壤水分含量空间变异的尺度性研究[J]. 农业工程学报,2005, 2l (8): 11-16.
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